Image Reconstruction In Photoacoustic Tomography Research Articles

Android mobile-platform-based image reconstruction for photoacoustic tomography.

In photoacoustic tomography (PAT), numerous reconstruction algorithms have been utilized to recover initial pressure rise distribution from the acquired pressure waves. In practice, most of these reconstructions are carried out on a desktop/workstation and the mobile-based reconstructions are far-flung. In recent years, mobile phones are becoming so ubiquitous, and most of them encompass a higher computing ability. Hence, realizing PAT image reconstruction on a mobile platform is intrinsic, and it will enhance the adaptability of PAT systems with point-of-care applications. To implement PAT image reconstruction in Android-based mobile platforms. For implementing PAT image reconstruction in Android-based mobile platforms, we proposed an Android-based application using Python to perform beamforming process in Android phones. The performance of the developed application was analyzed on different mobile platforms using both simulated and experimental datasets. The results demonstrate that the developed algorithm can accomplish the image reconstruction of in vivo small animal brain dataset in 2.4s. Furthermore, the developed application reconstructs PAT images with comparable speed and no loss of image quality compared to that on a laptop. Employing a two-fold downsampling procedure could serve as a viable solution for reducing the time needed for beamforming while preserving image quality with minimal degradation. We proposed an Android-based application that achieves image reconstruction on cheap, small, and universally available phones instead of relatively bulky expensive desktop computers/laptops/workstations. A beamforming speed of 2.4s is achieved without hampering the quality of the reconstructed image.

Fast and efficient PAT image reconstruction algorithms: A comparative performance analysis

Medical imaging technology efficiency is determined by how accurate, rapid, invasive, or non-invasive it is. Photoacoustic tomography (PAT) is a noninvasive emerging technology for medical imaging that is able to produce images with high resolution and high contrast in long penetration depths. In PAT, image reconstruction accuracy relies on the accuracy of the system model. Accurate model-based methods can be made use of for efficiently reconstructing a PAT image from the sensor measurements using a pseudo-spectral method which is suggested to produce exactly the same results as that of the k-Wave toolbox in MATLAB. Compressive sensing has been successfully applied for medical image reconstruction as it allows for sparse sampling and sparse reconstruction. Compressive sensing combined with PAT allows for deep tissue imaging at higher resolution at a much lower computational time. A fast and efficient PAT image reconstruction compressive sensing algorithm is needed to be developed, which can image dense tissues (such as breast tissue) rapidly, for early detection of cancer. In this paper, relaxed- Basis pursuit using alternating direction method of multipliers and split Bregman formulation of the total variation regularized least-squares problem is proposed and is observed to work under practical scenarios with fewer samples and sensors. Moreover, a comparative study for the performance analysis of the different compressive sensing algorithms, both iterative and non-iterative, is carried out for a decreased number of sensors and samples. The proposed split Bregman total variation algorithm is fast and produces a high-resolution sparsity preserving image compared to the state-of-the-art algorithms. Moreover, for a reduced number of samples, the proposed relaxed-Basis Pursuit is desired and performs much better than all the algorithms discussed. Implementing Cholesky factorization to the proposed algorithms further reduces the computation time of the split-Bregman algorithm by 90%. The new methods outperform existing ones in practical circumstances recreating a high-resolution image, even when the number of sensors and samples is reduced.

Performance comparison of commonly used photoacoustic tomography reconstruction algorithms under various blurring conditions

A reconstructed image in photoacoustic tomography (PAT) imaging modality can be blurred because of some system-dependent effects – broad laser pulse, the limited bandwidth of the sensors, acoustically attenuating medium and transducers with finite-size apertures having Gaussian sensitivity profile. A robust deblurring method involving a system matrix is presented in this work. Image reconstruction was accomplished by using the backprojection (BP), interpolated time reversal (ITR), Tikhonov regularization (TH) and total variation minimization (TV) schemes. A modified version of the BP algorithm was also explored for image reconstruction for finite transducers. Each factor of blurring was considered at a time. For the model-based methods, the system matrix was constructed in presence of such a factor. Subsequently, the model matrix inversion method was deployed for image formation. Quantitative values of some standard metrics were computed to assess the performance of the reconstruction protocols. The visual inspection of the reconstructed images as well as numerical values of the figures of merit revealed that the BP and ITR techniques generally fail to remove blurring; however, the model-based methods can provide distortion-free images under various conditions. For example, the numerical values of the Pearson correlation coefficient (PCC) for all the cases showed a strong correlation between the nominal and reconstructed images for the TH and TV techniques compared to the BP and ITR methods. The TV method can be utilized in practice to facilitate the PAT image reconstruction free from system-dependent blurring.

Deep-learning-based multi-transducer photoacoustic tomography imaging without radius calibration

Pulsed laser diodes are used in photoacoustic tomography (PAT) as excitation sources because of their low cost, compact size, and high pulse repetition rate. In combination with multiple single-element ultrasound transducers (SUTs) the imaging speed of PAT can be improved. However, during PAT image reconstruction, the exact radius of each SUT is required for accurate reconstruction. Here we developed a novel deep learning approach to alleviate the need for radius calibration. We used a convolutional neural network (fully dense U-Net) aided with a convolutional long short-term memory block to reconstruct the PAT images. Our analysis on the test set demonstrates that the proposed network eliminates the need for radius calibration and improves the peak signal-to-noise ratio by ∼73% without compromising the image quality. In vivo imaging was used to verify the performance of the network.

Acoustic impact of the human skull on transcranial photoacoustic imaging.

With balanced spatial resolution, imaging depth, and functional sensitivity, photoacoustic tomography (PAT) hold great promise for human brain imaging. However, the strong acoustic attenuation and aberration of the human skull (∼8 mm thick) are longstanding technical challenges for PAT of the human brain. In this work, we numerically investigated the impacts of the stratified human skull on photoacoustic wave propagation (i.e., the forward model) and PAT image formation (i.e., the inverse model). We simulated two representative transcranial PAT implementations: photoacoustic computed tomography (PACT) and photoacoustic macroscopy (PAMac). In the forward model, we simulated the detailed photoacoustic wave propagation from a point or line source through a digital human skull. The wave attenuation, refraction, mode conversation, and reverberation were thoroughly investigated. In the inverse model, we reconstructed the transcranial PACT and PAMac images of a point or line target enclosed by the human skull. Our results demonstrate that transcranial PAMac suffers mainly from wave reverberation within the skull, leading to prolonged signal duration and reduced axial resolution. Transcranial PACT is more susceptible to the skull's acoustic distortion, mode conversion, and reverberation, which collectively lead to strong image artifacts and deteriorated spatial resolutions. We also found that PACT with a ring-shaped transducer array shows more tolerance of the skull's adverse impacts and can provide more accurate image reconstruction. Our results suggest that incorporating the skull's geometry and acoustic properties can improve transcranial PAT image reconstruction. We expect that our results have provided a more comprehensive understanding of the acoustic impact of the human skull on transcranial PAT.

Practical photoacoustic tomography: Realistic limitations and technical solutions

This article offers a perspective on photoacoustic tomography (PAT) under realistic scenarios. While PAT has gained much attention in preclinical and clinical research, most early works used image reconstruction techniques based on ideal assumptions, and thus these techniques may not be fully effective in real environments. In this work, we consider such non-ideal conditions as a limited view, limited bandwidth, lossy medium, or heterogeneous medium. More importantly, we use k-Wave simulation to numerically evaluate the effects of these limiting factors on various image reconstruction algorithms. Then, to enable more reliable PAT image reconstruction, we introduce recent techniques for mitigating each of the limiting conditions. We seek to emphasize the importance of working within these realistic limitations, and we encourage researchers to develop compensating solutions that advance PAT’s translation to real clinical environments.

Impacts of the murine skull on high-frequency transcranial photoacoustic brain imaging.

Non-invasive photoacoustic tomography (PAT) of mouse brains with intact skulls has been a challenge due to the skull's strong acoustic attenuation, aberration, and reverberation, especially in the high-frequency range (>15 MHz). In this paper, we systematically investigated the impacts of the murine skull on the photoacoustic wave propagation and on the PAT image reconstruction. We studied the photoacoustic acoustic wave aberration due to the acoustic impedance mismatch at the skull boundaries and the mode conversion between the longitudinal wave and shear wave. The wave's reverberation within the skull was investigated for both longitudinal and shear modes. In the inverse process, we reconstructed the transcranial photoacoustic computed tomography (PACT) and photoacoustic microscopy (PAM) images of a point target enclosed by the mouse skull, showing the skull's different impacts on both modalities. Finally, we experimentally validated the simulations by imaging an in vitro mouse skull phantom using representative transcranial PAM and PACT systems. The experimental results agreed well with the simulations and confirmed the accuracy of our forward and inverse models. We expect that our results will provide better understanding of the impacts of the murine skull on transcranial photoacoustic brain imaging and pave the ways for future technical improvements.

Pixel-wise deep learning for improving image reconstruction in photoacoustic tomography

Photoacoustic tomography involves absorption of pulsed light and subsequent generation of ultrasound, which when detected using an array of sensors can produce clinically useful images. Practical considerations limit the number of sensors and their “view” of the region of interest (ROI), which can result in significant reconstruction artifacts. Iterative-reconstruction methods can improve image quality but are computationally expensive. Another approach to improve reconstructed images is to use convolution neural networks (CNN) as a post-processing step for removing artifacts. However, missing or heavily obscured features typically cannot be recovered using this approach. We present a new pixel-wise deep learning (PDL) approach that employs pixel-wise interpolation to window ROI-specific raw photoacoustic data and then directly performs the image reconstruction within the CNN framework. The utility of this approach was demonstrated on simulated photoacoustic data from a 64-element semi-circular sensor array. The training and testing datasets comprised of 500 images from a synthetic vasculature phantom and 50 images of an anatomically realistic vasculature obtained from micro-CT images, respectively. The structural similarity index of the PDL-reconstructed images (0.91 ± 0.03) indicated superior image quality compared to those obtained using the iterative reconstruction (0.82 ± 0.09) and CNN-based artifact removal (0.79 ± 0.07).

Deep-learning framework and acoustic reflector for improved limited-view and sparse photoacoustic tomography

Photoacoustic imaging is a hybrid imaging modality that relies upon optical absorption of pulsed light and subsequent thermoelastic generation of ultrasound. The detection of the induced acoustic waves outside the tissue enables image reconstruction. A major challenge encountered in photoacoustic tomography (PAT) lies in the inability to acquire complete projection data from the region of interest due to both the limited view and sparsity of available ultrasonic sensors. The resulting images are characterized by severe artifacts and poor quality. In this work, we examined the utility of incorporating an acoustic reflector to address the limited view problem and to train a convolutional neural network (CNN) to improve PAT image reconstruction from sparsely sampled data. Photoacoustic wave propagation was simulated in MATLAB using the k-Wave toolbox. We compared the performance of a sparse linear transducer array (with and without reflector) to that of a circular transducer array. The structural similarity index (SSI) was used as a metric for evaluating image quality. The combination of a curved reflector and artifact-removal using a CNN improved the quality of PAT images from the linear configuration. The resulting mean SSI value (0.859) was comparable to that achieved using the circular transducer array (0.926).

Analysis of the Linearized Problem of Quantitative Photoacoustic Tomography

Quantitative image reconstruction in photoacoustic tomography requires the solution of a coupled physics inverse problem involving light transport and acoustic wave propagation. In this paper we address this issue employing the radiative transfer equation as an accurate model for light transport. As main theoretical results, we derive several stability and uniqueness results for the linearized inverse problem. We consider the case of single illumination as well as the case of multiple illuminations assuming full or partial data. The numerical solution of the linearized problem is much less costly than the solution of the nonlinear problem. We present numerical simulations supporting the derived stability results for the linearized problem of quantitative photoacoustic tomography.

A photoacoustic imaging reconstruction method based on directional total variation with adaptive directivity

BackgroundIn photoacoustic tomography (PAT), total variation (TV) based iteration algorithm is reported to have a good performance in PAT image reconstruction. However, classical TV based algorithm fails to preserve the edges and texture details of the image because it is not sensitive to the direction of the image. Therefore, it is of great significance to develop a new PAT reconstruction algorithm to effectively solve the drawback of TV.MethodsIn this paper, a directional total variation with adaptive directivity (DDTV) model-based PAT image reconstruction algorithm, which weightedly sums the image gradients based on the spatially varying directivity pattern of the image is proposed to overcome the shortcomings of TV. The orientation field of the image is adaptively estimated through a gradient-based approach. The image gradients are weighted at every pixel based on both its anisotropic direction and another parameter, which evaluates the estimated orientation field reliability. An efficient algorithm is derived to solve the iteration problem associated with DDTV and possessing directivity of the image adaptively updated for each iteration step.Results and conclusionSeveral texture images with various directivity patterns are chosen as the phantoms for the numerical simulations. The 180-, 90- and 30-view circular scans are conducted. Results obtained show that the DDTV-based PAT reconstructed algorithm outperforms the filtered back-projection method (FBP) and TV algorithms in the quality of reconstructed images with the peak signal-to-noise rations (PSNR) exceeding those of TV and FBP by about 10 and 18 dB, respectively, for all cases. The Shepp–Logan phantom is studied with further discussion of multimode scanning, convergence speed, robustness and universality aspects. In-vitro experiments are performed for both the sparse-view circular scanning and linear scanning. The results further prove the effectiveness of the DDTV, which shows better results than that of the TV with sharper image edges and clearer texture details. Both numerical simulation and in vitro experiments confirm that the DDTV provides a significant quality improvement of PAT reconstructed images for various directivity patterns.

A Multi-Grid Iterative Method for Photoacoustic Tomography.

Inspired by the recent advances on minimizing nonsmooth or bound-constrained convex functions on models using varying degrees of fidelity, we propose a line search multi-grid (MG) method for full-wave iterative image reconstruction in photoacoustic tomography (PAT) in heterogeneous media. To compute the search direction at each iteration, we decide between the gradient at the target level, or alternatively an approximate error correction at a coarser level, relying on some predefined criteria. To incorporate absorption and dispersion, we derive the analytical adjoint directly from the first-order acoustic wave system. The effectiveness of the proposed method is tested on a total-variation penalized Iterative Shrinkage Thresholding algorithm (ISTA) and its accelerated variant (FISTA), which have been used in many studies of image reconstruction in PAT. The results show the great potential of the proposed method in improving speed of iterative image reconstruction.

On the adjoint operator in photoacoustic tomography

Photoacoustic tomography (PAT) is an emerging biomedical imaging from coupled physics technique, in which the image contrast is due to optical absorption, but the information is carried to the surface of the tissue as ultrasound pulses. Many algorithms and formulae for PAT image reconstruction have been proposed for the case when a complete data set is available. In many practical imaging scenarios, however, it is not possible to obtain the full data, or the data may be sub-sampled for faster data acquisition. In such cases, image reconstruction algorithms that can incorporate prior knowledge to ameliorate the loss of data are required. Hence, recently there has been an increased interest in using variational image reconstruction. A crucial ingredient for the application of these techniques is the adjoint of the PAT forward operator, which is described in this article from physical, theoretical and numerical perspectives. First, a simple mathematical derivation of the adjoint of the PAT forward operator in the continuous framework is presented. Then, an efficient numerical implementation of the adjoint using a k-space time domain wave propagation model is described and illustrated in the context of variational PAT image reconstruction, on both 2D and 3D examples including inhomogeneous sound speed. The principal advantage of this analytical adjoint over an algebraic adjoint (obtained by taking the direct adjoint of the particular numerical forward scheme used) is that it can be implemented using currently available fast wave propagation solvers.

Exponential filtering of singular values improves photoacoustic image reconstruction

Model-based image reconstruction techniques yield better quantitative accuracy in photoacoustic image reconstruction. In this work, an exponential filtering of singular values was proposed for carrying out the image reconstruction in photoacoustic tomography. The results were compared with widely popular Tikhonov regularization, time reversal, and the state of the art least-squares QR-based reconstruction algorithms for three digital phantom cases with varying signal-to-noise ratios of data. It was shown that exponential filtering provides superior photoacoustic images of better quantitative accuracy. Moreover, the proposed filtering approach was observed to be less biased toward the regularization parameter and did not come with any additional computational burden as it was implemented within the Tikhonov filtering framework. It was also shown that the standard Tikhonov filtering becomes an approximation to the proposed exponential filtering.

Full-Wave Iterative Image Reconstruction in Photoacoustic Tomography With Acoustically Inhomogeneous Media

Existing approaches to image reconstruction in photoacoustic computed tomography (PACT) with acoustically heterogeneous media are limited to weakly varying media, are computationally burdensome, and/or cannot effectively mitigate the effects of measurement data incompleteness and noise. In this work, we develop and investigate a discrete imaging model for PACT that is based on the exact photoacoustic (PA) wave equation and facilitates the circumvention of these limitations. A key contribution of the work is the establishment of a procedure to implement a matched forward and backprojection operator pair associated with the discrete imaging model, which permits application of a wide-range of modern image reconstruction algorithms that can mitigate the effects of data incompleteness and noise. The forward and backprojection operators are based on the k-space pseudospectral method for computing numerical solutions to the PA wave equation in the time domain. The developed reconstruction methodology is investigated by use of both computer-simulated and experimental PACT measurement data.

Photoacoustic computed tomography correcting for heterogeneity and attenuation.

We report an investigation of image reconstruction in photoacoustic tomography for objects that possess heterogeneous material and acoustic attenuation distributions. When the object contains materials, such as bone and soft-tissue, that are modeled using power law attenuation models with distinct exponents, we demonstrate that the effects of acoustic attenuation due to the most strongly attenuating material can be compensated for if the attenuation of the other less attenuating material(s) are neglected. Experiments with phantom objects are presented to validated our findings.

Image reconstruction in photoacoustic tomography involving layered acoustic media

Photoacoustic tomography (PAT), also known as thermoacoustic or optoacoustic tomography, is a rapidly emerging biomedical imaging technique that combines optical image contrast with ultrasound detection principles. Most existing reconstruction algorithms for PAT assume the object of interest possesses homogeneous acoustic properties. The images produced by such algorithms can contain significant distortions and artifacts when the object's acoustic properties are spatially variant. In this work, we establish an image reconstruction formula for PAT applications in which a planar detection surface is employed and the to-be-imaged optical absorber is embedded in a known planar layered acoustic medium. The reconstruction formula is exact in a mathematical sense and accounts for multiple acoustic reflections between the layers of the medium. Computer-simulation studies are conducted to demonstrate and investigate the proposed method.

SU‐GG‐I‐168: Image Reconstruction in an Attenuating Medium with Two Different Attenuation Coefficients Using Photoacoustic Tomography

Purpose: The goal of this research is to incorporate the effect of attenuation of the surrounding medium and accurately reconstruct the optical absorption distribution of the photoacoustic source. Method and Materials: Photoacoustic tomography (PAT) reconstructs an image of the optical absorption of an object using the pressure signals that are generated when the object is illuminated via pulsed electromagnetic radiation. The effect of ultrasonic attenuation can be significant since photoacoustic tomography uses broadband detection and the ultrasonic attenuation varies with frequency. Previous studies have related the detected pressure to the optical absorption coefficient when there is uniform attenuation. In this work, we specifically consider the practically important problem of an photoacoustic source embedded in a medium (such as tissue) with an attenuation coefficient, surrounded by a matching medium (such as water) with a different attenuation coefficient. The photoacoustic pressure that is measured by the detector in this case is not directly related to the optical absorption coefficient of the source. We have derived an analytical expression relating the photoacoustic pressure in the contact plane to the pressure in the detector plane. We can then reconstruct an image using the calculated pressure on the contact plane and the standard image reconstruction techniques for PAT. Results: Preliminary results in 2D suggest that the analytic expression that we derived to relate the photoacoustic pressure on the detection plane with that in the contact plane provides a suitable method for obtaining the photoacoustic source lying in an attenuating medium. Conclusion: The attenuation effect of the surrounding medium have been incorporated into image reconstruction in photoacoustic tomography. This work will be useful to accurately reconstruct the image in PAT when the attenuation coefficient of the object (such as tumor) differs from that of the surrounding medium (healthy tissue).

Image reconstruction in photoacoustic tomography with variable speed of sound using a higher-order geometrical acoustics approximation

Previous research correcting for variable speed of sound in photoacoustic tomography (PAT) based on a generalized radon transform (GRT) model assumes first-order geometrical acoustics (GA) approximation. In the GRT model, the pressure is related to the optical absorption, in an acoustically inhomogeneous medium, through integration over nonspherical isochronous surfaces. Previous research based on the GRT model assumes that the path taken by acoustic rays is linear and neglects amplitude perturbations to the measured pressure. We have derived a higher-order GA expression that takes into account the first-order effect in the amplitude of the measured signal and higher-order perturbation to the travel times. The higher-order perturbation to travel time incorporates the effect of ray bending. Incorrect travel times can lead to image distortion and blurring. These corrections are expected to impact image quality and quantitative PAT. We have previously shown that travel-time corrections in 2-D suggest that perceivable differences in the isochronous surfaces can be seen when the second-order travel-time perturbations are taken into account with a 10% speed-of-sound variation. In this work, we develop iterative image reconstruction algorithms that incorporate this higher-order GA approximation assuming that the speed of sound map is known. We evaluate the effect of higher-order GA approximation on image quality and accuracy.

Application of inverse source concepts to photoacoustic tomography

Photoacoustic tomography (PAT), also known as optoacoustic or thermoacoustic tomography, is a hybrid imaging technique that possesses great potential for a wide range of biomedical imaging applications. Image reconstruction in PAT is tantamount to solving an inverse source problem, where the source represents the optical energy absorption distribution in the object that is induced by an interrogating pulsed optical waveform. In this work, we re-examine the PAT image reconstruction problem from a Fourier domain perspective by use of established time-harmonic inverse source concepts. A mathematical relationship between the photoacoustic pressure wavefield data on an aperture that encloses the object and the three-dimensional Fourier transform of the optical absorption distribution evaluated on a collection of concentric spheres is investigated. In addition to providing a framework for deriving both exact and approximate analytic reconstruction formulae, we demonstrate that this mapping provides an intuitive means of understanding certain spatial resolution characteristics of PAT.